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  • Segmentation of Vestibular Schwannoma from Magnetic Resonance Imaging: An Open Annotated Dataset and Baseline Algorithm (Vestibular-Schwannoma-SEG)
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This collection contains a labelled dataset of MRI images collected on 242 consecutive patients with vestibular schwannoma (VS) undergoing Gamma Knife stereotactic radiosurgery (GK SRS). The structural images included contrast-enhanced T1-weighted (ceT1) images and high-resolution T2-weighted (hrT2) images. Each imaging dataset is accompanied by the patient’s radiation therapy (RT) dataset including the RTDose, RTStructures and RTPlan. All structures were manually segmented in consensus by the treating neurosurgeon and physicist using both the ceT1 and hrT2 images. The value of this collection is to provide clinical image data including fully annotated tumour segmentations to facilitate the development and validation of automated segmentation frameworks. It may also be used for research relating to radiation treatment.

Imaging data from consecutive patients with a single sporadic VS treated with GK SRS between the years of 2012 and 2018 were screened for the study. All adult patients older than 18 years with a single unilateral VS treated with GK SRS were eligible for inclusion in the study, including patients who had previously undergone operative surgical treatment. In total, 248 patients met these initial inclusion criteria including 51 patients who had previously undergone surgery. Patients were only included in the study if their pre-treatment image acquisition dataset was complete; 2 patients were thus excluded because of incomplete datasets.

The images were obtained on a 32-channel Siemens Avanto 1.5T scanner using a Siemens single-channel head coil.

  • Contrast-enhanced T1-weighted imaging was performed with an MPRAGE sequence with TR / TE / TI = 1900 ms / 2.97 ms / 1100 ms, in-plane resolution of 0.4 × 0.4 mm, in-plane matrix of 512 × 512, and slice thickness of 1.0–1.5 mm
  • High-resolution T2-weighted imaging was performed with a 3D CISS or FIESTA sequence with TR / TE 9.4 ms / 4.23 ms, in-plane resolution of approximately 0.5 × 0.5 mm, an in-plane matrix of 384 × 384 or 448 × 448, and slice thickness of 1.0–1.5 mm.

All manual segmentations were performed using Gamma Knife planning software (Leksell GammaPlan, Elekta, Sweden) that employs an in-plane semiautomated segmentation method. Using this software, each axial slice was manually segmented in turn.

Please see the github respository link which contains a script to organize the downloaded data into a more convenient folder structure and a script to convert the downloaded DICOM images and segmentations into NIFTI format. Moreover, the repository contains an algorithm for automatic segmentation of VS with deep learning, adapted to this data set. The applied neural network is based on the 2.5D UNet described in \cite{Shapey2019} and has been adapted to yield improved segmentation results. Our implementation uses MONAI, a freely available, PyTorch-based framework for deep learning in healthcare imaging (Project MONAI). This new implementation was devised to provide a starting point for researchers interested in automatic segmentation using state-of-the art deep learning frameworks for medical image processing.


  • Site to provide acknowledgements including grant information.

Data Access

Click the  Download button to save a ".tcia" manifest file to your computer, which you must open with the NBIA Data Retriever . Click the Search button to open our Data Portal, where you can browse the data collection and/or download a subset of its contents.

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Images, Segmentations, and Radiation Therapy Structures (DICOM, XX.X GB)


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Detailed Description

Image Statistics



Number of Participants


Number of Studies


Number of Series


Number of Images


Images Size (GB)

Citations & Data Usage Policy

Users of this data must abide by the TCIA Data Usage Policy and the Creative Commons Attribution 4.0 International License under which it has been published. Attribution should include references to the following citations:

Data Citation

Shapey, J., Kujawa, A., Dorent, R., Wang, G., Bisdas, S., Dimitriadis, A., Grishchuck, D., Paddick, I., Kitchen, N., Bradford, R., Saeed, S., Ourselin, S., Vercauteren, T. (2020). Segmentation of vestibular schwannoma from magnetic resonance imaging: An open annotated dataset and baseline algorithm (Vestibular-Schwannoma-SEG) [Data Set]. The Cancer Imaging Archive. DOI Pending.​

Publication Citation

Shapey, J., Wang, G., Dorent, R., Dimitriadis, A., Li, W., Paddick, I., Kitchen, N., Bisdas, S., Saeed, S. R., Ourselin, S., Bradford, R., and Vercauteren, T. (2019). An artificial intelligence framework for automatic segmentation and volumetry of vestibular schwannomas from contrast-enhanced T1-weighted and high-resolution T2-weighted MRI. Journal of Neurosurgery JNS , 1-9, available from: <>

TCIA Citation

Clark K, Vendt B, Smith K, Freymann J, Kirby J, Koppel P, Moore S, Phillips S, Maffitt D, Pringle M, Tarbox L, Prior F. The Cancer Imaging Archive (TCIA): Maintaining and Operating a Public Information Repository, Journal of Digital Imaging, Volume 26, Number 6, December, (2013), pp 1045-1057. DOI:


This work was supported by Wellcome Trust (203145Z/16/Z, 203148/Z/16/Z, WT106882) and EPSRC (NS/A000050/1, NS/A000049/1) funding. Tom Vercauteren is also supported by a Medtronic/Royal Academy of Engineering Research Chair (RCSRF1819\7\34).

Other Publications Using This Data

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Version 1 (Current): Updated yyyy/mm/dd

Data TypeDownload all or Query/Filter
Images, Segmentations, and Radiation Therapy Structures (DICOM, xx.x GB)

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